城市交通监测在安全分析、拥堵管理和事故响应中起着关键作用。Robotaxi（自动驾驶出租车）部署的增长为网络级交通监测提供了新机遇。尽管 Robotaxi 主要设计用于载客服务，但其也可被用作途经传感器以收集交通数据。与传统基础设施传感器或探测车辆相比，Robotaxi 车队构成了一个协同感知环境，能够集体采集时空连续的交通信息。本文提出了一种新颖的动态 Robotaxi 路径规划框架，明确将交通监测任务纳入目标函数。该框架引入了：(1) 一种与 Robotaxi 感知能力相匹配的基于单元的网络表示方法；(2) 一种用于量化 Robotaxi 时空覆盖率的单元级监测指标；以及 (3) 一种混合整数线性规划 (MILP) 模型，旨在同时最小化时变行程时间并最大化交通监测性能。研究在 SUMO 中构建了一个 5x5 的城市网格网络，并在三种 Robotaxi 市场渗透率（2%、5% 和 10%）及一系列目标权重组合下评估了该框架。结果表明，在目标函数中纳入时空网络覆盖率能有效提升交通监测性能。值得注意的是，通过合理设置两个目标之间的权重，可以同时改善监测性能和 Robotaxi 的平均速度。这表明更好的网络监测有助于更准确的交通状态预测和提升出行效率。这种双赢局面可能激励 Robotaxi 运营商贡献其车辆作为交通监测的途经传感器。

Urban traffic monitoring plays a critical role in safety analysis, congestion management, and incident response. The growing deployment of robotaxis creates a new opportunity for network-level traffic monitoring. Although robotaxis are primarily designed to serve passengers, they can also be leveraged as drive-by sensors to collect traffic data. Compared to conventional infrastructure sensors or probe vehicles, a fleet of robotaxis forms a cooperative perception environment, which can collectively gather spatially and temporally continuous traffic information. This paper proposes a novel dynamic robotaxi routing framework that explicitly incorporates traffic monitoring tasks as an objective. The framework introduces: (1) a cell-based network representation that aligns with sensing capabilities of robotaxis; (2) a cell-level monitoring metric to quantify spatiotemporal robotaxi coverage; and (3) a mixed-integer linear programming (MILP) formulation that jointly minimizes time-dependent travel time and maximizes traffic monitoring performance. A 5 by 5 urban grid network is built in SUMO to evaluate the framework under three robotaxi market penetration rates (2%, 5%, and 10%) with a range of objective weight combinations. Results show that incorporating spatiotemporal network coverage in the objective function can effectively improve the traffic monitoring performance. Interestingly, with appropriate weights between the two objectives, monitoring performance and robotaxi average speed can be improved simultaneously. This suggests better network monitoring leads to more accurate traffic state prediction and improved mobility. This win-win situation could incentivize robotaxi operators to contribute their vehicles as drive-by sensors for traffic monitoring.